Another group [1]}, [2]}, [3]} rely on an actionness measure, i.e., a pixel-wise probability of containing any action. To estimate actionness, they use low-level cues such as optical flow [3]}, CNNs with a two-stream architecture [2]} or RNNs [2]}. They extract action tubes by thresholding the actionness scores [1]} or by using a maximum set coverage [3]}. The output is a rough localization of the action as it is based on noisy pixel-level maps.
